{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "source": [
    "# 简单RAG介绍\n",
    "\n",
    "检索增强生成（RAG）是一种混合方法，它结合了信息检索和生成模型。通过融入外部知识，RAG提升了语言模型的性能，改善了准确性和事实正确性。\n",
    "\n",
    "在简单RAG设置中，我们遵循以下步骤：\n",
    "\n",
    "1. **数据摄取**：加载和预处理文本数据。\n",
    "2. **分块**：将数据分解为更小的块以提高检索性能。\n",
    "3. **嵌入创建**：使用嵌入模型将文本块转换为数字表示。\n",
    "4. **语义搜索**：基于用户查询检索相关块。\n",
    "5. **响应生成**：使用语言模型根据检索的文本生成响应。\n",
    "\n",
    "This notebook implements a Simple RAG approach, evaluates the model’s response, and explores various improvements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置环境\n",
    "我们首先导入必要的库。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fitz\n",
    "import os\n",
    "import numpy as np\n",
    "import json\n",
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从PDF文件中提取文本\n",
    "要实现RAG，我们首先需要文本数据源。在本例中，我们使用PyMuPDF库从PDF文件中提取文本。\n",
    "PyMuPDF 是一款功能强大的 Python PDF 处理库，而 fitz 是其主要的模块名。它能够快速渲染 PDF 内容，并且提供了对多种文档格式（如 PDF、XPS、EPUB 等）的读取、写入和修改功能，在文档处理领域应用广泛。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_from_pdf(pdf_path):\n",
    "    \"\"\"\n",
    "    从PDF文件中提取文本。\n",
    "\n",
    "    参数:\n",
    "    pdf_path (str): PDF文件的路径。\n",
    "\n",
    "    返回:\n",
    "    str: 从PDF中提取的文本。\n",
    "    \"\"\"\n",
    "    # 打开PDF文件\n",
    "    mypdf = fitz.open(pdf_path)\n",
    "    all_text = \"\"  # 初始化一个空字符串来存储提取的文本\n",
    "\n",
    "    # 遍历PDF中的每一页\n",
    "    for page_num in range(mypdf.page_count):\n",
    "        page = mypdf[page_num]  # 获取页面\n",
    "        text = page.get_text(\"text\")  # 从页面提取文本\n",
    "        all_text += text  # 将提取的文本追加到all_text字符串\n",
    "\n",
    "    return all_text  # 返回提取的文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Ollama服务正在运行\n",
      "已安装的模型: ['qwen3:8b', 'qwen2.5:7b', 'deepseek-r1:7b', 'quentinz/bge-large-zh-v1.5:latest', 'bge-m3:latest', 'linux6200/bge-reranker-v2-m3:latest', 'deepseek-r1:1.5b', 'deepseek-r1:8b', 'nomic-embed-text:latest']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查Ollama服务是否运行\n",
    "import requests\n",
    "import time\n",
    "\n",
    "def check_ollama_service():\n",
    "    \"\"\"检查Ollama服务是否正在运行\"\"\"\n",
    "    try:\n",
    "        response = requests.get(\"http://localhost:11434/api/tags\", timeout=5)\n",
    "        if response.status_code == 200:\n",
    "            print(\"✅ Ollama服务正在运行\")\n",
    "            models = response.json().get('models', [])\n",
    "            model_names = [model['name'] for model in models]\n",
    "            print(f\"已安装的模型: {model_names}\")\n",
    "            return True\n",
    "        else:\n",
    "            print(\"❌ Ollama服务未响应\")\n",
    "            return False\n",
    "    except requests.exceptions.RequestException:\n",
    "        print(\"❌ 无法连接到Ollama服务。请确保Ollama正在运行。\")\n",
    "        print(\"请运行: ollama serve\")\n",
    "        return False\n",
    "\n",
    "# 检查服务状态\n",
    "check_ollama_service()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对提取的文本进行分块\n",
    "一旦我们获得了提取的文本，我们将其分割成更小的、重叠的块，以提高检索准确性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunk_text(text, n, overlap):\n",
    "    \"\"\"\n",
    "    将给定文本分割成具有重叠的n个字符的段落。\n",
    "\n",
    "    参数:\n",
    "    text (str): 要分块的文本。\n",
    "    n (int): 每个块中的字符数。\n",
    "    overlap (int): 块之间重叠的字符数。\n",
    "\n",
    "    返回:\n",
    "    List[str]: 文本块列表。\n",
    "    \"\"\"\n",
    "    chunks = []  # 初始化一个空列表来存储块\n",
    "    \n",
    "    # 以(n - overlap)的步长循环遍历文本\n",
    "    for i in range(0, len(text), n - overlap):\n",
    "        # 将从索引i到i+n的文本块追加到chunks列表\n",
    "        chunks.append(text[i:i + n])\n",
    "\n",
    "    return chunks  # 返回文本块列表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置Ollama客户端\n",
    "我们初始化Ollama客户端来生成嵌入向量和响应。本地运行的Ollama服务可以兼容OpenAI API格式。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用本地Ollama服务初始化OpenAI客户端\n",
    "client = OpenAI(\n",
    "    base_url=\"http://localhost:11434/v1/\",\n",
    "    api_key=\"ollama\"  # Ollama不需要真实的API密钥，但客户端需要一个值\n",
    ")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "## 准备Ollama模型\n",
    "在运行代码之前，请确保您已经安装了Ollama并下载了所需的模型：\n",
    "\n",
    "```bash\n",
    "# 下载嵌入模型\n",
    "ollama pull bge-m3:latest\n",
    "\n",
    "# 下载语言生成模型  \n",
    "ollama pull qwen2.5:7b\n",
    "```\n",
    "\n",
    "您可以运行以下命令来验证模型是否已安装：\n",
    "```bash\n",
    "ollama list\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从PDF文件中提取和分块文本\n",
    "现在，我们加载PDF，提取文本，并将其分割为块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本块数量: 13\n",
      "\n",
      "第一个文本块:\n",
      "了解人工智能\n",
      "第 1 章：人工智能简介\n",
      "人工智能（AI）是指数字计算机或计算机控制的机器人执行通常与智能生物相关的任务的能力。该术语\n",
      "经常被用于开发具有人类特有智力过程的系统的项目，如推理、发现意义、概括或从过去的经验中学习\n",
      "的能力。过去几十年来，计算能力和数据可用性的进步大大加快了人工智能的发展和部署。\n",
      "历史背景\n",
      "人工智能的概念已经存在了几个世纪，通常在神话和小说中有所描述。然而，人工智能研究的正式领域\n",
      "始于 20 世纪中期。1956 年的达特茅斯研讨会被广泛认为是人工智能的发源地。早期的人工智能研究侧\n",
      "重于解决问题和符号方法。20 世纪 80 年代，专家系统兴起，而 20 世纪 90 年代和 21 世纪则带来了机器\n",
      "学习和神经网络的进步。最近在深度学习方面取得的突破彻底改变了这一领域。\n",
      "现代观察\n",
      "现代人工智能系统在日常生活中越来越普遍。从 Siri 和 Alexa 等虚拟助手到流媒体服务和社交媒体上的\n",
      "推荐算法，人工智能正在影响着我们的生活、工作和互动方式。自动驾驶汽车、先进的医疗诊断技术和\n",
      "复杂的金融建模工具的发展表明，人工智能的应用范围越来越广。人们对道德影响、偏见和失业的担忧\n",
      "也日益突出。\n",
      "第 2 章：人工智能的核心概念\n",
      "机器学习\n",
      "机器学习（ML）是人工智能的一个子集，其重点是使系统能够在没有明确编程的情况下从数据中学习\n",
      "。ML 算法可以识别模式、进行预测，并随着时间的推移，在接触更多数据后提高性能。\n",
      "监督学习\n",
      "在监督学习中，算法是在有标签的数据上进行训练的，其中输入数据与正确的输出配对。这样，算法就\n",
      "能学习输入和输出之间的关系，并对新的、未见过的数据进行预测。这方面的例子包括图像分类和垃圾\n",
      "邮件检测。\n",
      "无监督学习\n",
      "无监督学习算法是在无标记数据上进行训练的，算法必须在没有明确指导的情况下发现数据中的模式和\n",
      "结构。常见的技术包括聚类（对相似数据点进行分组）和降维（在保留重要信息的同时减少变量数量）\n",
      "。\n",
      "Subscribe to DeepL Pro to translate larger documents.\n",
      "Visit www.DeepL.com/pro for more information.\n",
      "强化学习\n",
      "强化学习是指训练一个代理在环境中做出决策，使奖励最大化。代理通过尝试和错误进行学习，并以奖\n",
      "励或惩罚的形式获得反馈。这种方法可用于游戏、机器人和资源管理\n"
     ]
    }
   ],
   "source": [
    "# 定义PDF文件的路径\n",
    "pdf_path = \"data/AI_Information_zh.pdf\"\n",
    "\n",
    "# 从PDF文件中提取文本\n",
    "extracted_text = extract_text_from_pdf(pdf_path)\n",
    "\n",
    "# 将提取的文本分块为1000个字符的段落，重叠200个字符\n",
    "text_chunks = chunk_text(extracted_text, 1000, 200)\n",
    "\n",
    "# 打印创建的文本块数量\n",
    "print(\"文本块数量:\", len(text_chunks))\n",
    "\n",
    "# 打印第一个文本块\n",
    "print(\"\\n第一个文本块:\")\n",
    "print(text_chunks[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 为文本块创建嵌入向量\n",
    "嵌入向量将文本转换为数值向量，从而实现高效的相似性搜索。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CreateEmbeddingResponse(data=[Embedding(embedding=[0.005304028, -0.022072658, 0.024066841, -0.027917756, 0.0027943768, 0.031122072, -0.005144126, -0.017196596, 0.027245944, -0.039656565, -0.02619838, -0.027206207, -0.027672796, 0.04553062, 0.02008669, 0.024832731, 0.002307755, -0.0054144533, 0.01282714, 0.015013644, 0.060836736, -0.06783611, -0.01818378, 0.0029019746, -0.03426075, 0.06274168, -0.019876506, -0.04268281, 0.080643624, 0.008346484, 0.02403079, 0.0055039423, 0.008660587, 0.025370574, 0.046826098, -0.020865994, -0.024953753, -0.018829264, 0.038314134, 0.033696145, 0.038169708, 0.023671323, 0.013412163, -0.024148706, -0.0020353086, 0.05405081, 0.0010873075, -0.00033712396, 0.01947742, 0.023152987, -0.015215733, -0.008207907, -0.0030833634, -0.0038701377, 0.01097908, 0.030140197, 0.00371927, -0.013919853, -0.040447664, -0.03676514, -0.012361221, 0.07866443, -0.022995673, -0.0025750955, -0.03672243, -0.04485249, 0.039953798, -0.045086764, -0.010660134, 0.07349349, 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object='embedding')], model='quentinz/bge-large-zh-v1.5:latest', object='list', usage=Usage(prompt_tokens=6656, total_tokens=6656))\n"
     ]
    }
   ],
   "source": [
    "def create_embeddings(text, model=\"quentinz/bge-large-zh-v1.5:latest\"):\n",
    "    \"\"\"\n",
    "    使用指定的Ollama模型为给定文本创建嵌入向量。\n",
    "\n",
    "    参数:\n",
    "    text (str): 需要创建嵌入向量的输入文本。\n",
    "    model (str): 用于创建嵌入向量的模型。默认是\"bge-m3:latest\"。\n",
    "\n",
    "    返回:\n",
    "    dict: 包含嵌入向量的Ollama API响应。\n",
    "    \"\"\"\n",
    "    # 使用指定模型为输入文本创建嵌入向量\n",
    "    response = client.embeddings.create(\n",
    "        model=model,\n",
    "        input=text\n",
    "    )\n",
    "\n",
    "    return response  # 返回包含嵌入向量的响应\n",
    "\n",
    "# 为文本块创建嵌入向量\n",
    "response = create_embeddings(text_chunks)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 执行语义搜索\n",
    "我们实现余弦相似度来找到与用户查询最相关的文本块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cosine_similarity(vec1, vec2):\n",
    "    \"\"\"\n",
    "    计算两个向量之间的余弦相似度。\n",
    "\n",
    "    参数:\n",
    "    vec1 (np.ndarray): 第一个向量。\n",
    "    vec2 (np.ndarray): 第二个向量。\n",
    "\n",
    "    返回:\n",
    "    float: 两个向量之间的余弦相似度。\n",
    "    \"\"\"\n",
    "    # 计算两个向量的点积并除以它们范数的乘积\n",
    "    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def semantic_search(query, text_chunks, embeddings, k=5):\n",
    "    \"\"\"\n",
    "    使用给定查询和嵌入向量对文本块进行语义搜索。\n",
    "\n",
    "    参数:\n",
    "    query (str): 语义搜索的查询。\n",
    "    text_chunks (List[str]): 要搜索的文本块列表。\n",
    "    embeddings (List[dict]): 文本块的嵌入向量列表。\n",
    "    k (int): 要返回的顶部相关文本块数量。默认为5。\n",
    "\n",
    "    返回:\n",
    "    List[str]: 基于查询的前k个最相关文本块列表。\n",
    "    \"\"\"\n",
    "    # 为查询创建嵌入向量\n",
    "    query_embedding = create_embeddings(query).data[0].embedding\n",
    "    similarity_scores = []  # 初始化列表来存储相似度分数\n",
    "\n",
    "    # 计算查询嵌入和每个文本块嵌入之间的相似度分数\n",
    "    for i, chunk_embedding in enumerate(embeddings):\n",
    "        similarity_score = cosine_similarity(np.array(query_embedding), np.array(chunk_embedding.embedding))\n",
    "        similarity_scores.append((i, similarity_score))  # 追加索引和相似度分数\n",
    "\n",
    "    # 按降序排列相似度分数\n",
    "    similarity_scores.sort(key=lambda x: x[1], reverse=True)\n",
    "    print(similarity_scores)\n",
    "    # 获取前k个最相似文本块的索引\n",
    "    top_indices = [index for index, _ in similarity_scores[:k]]\n",
    "    # 返回前k个最相关的文本块\n",
    "    return [text_chunks[index] for index in top_indices]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在提取的文本块上运行查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(11, np.float64(0.5524327167704531)), (0, np.float64(0.4990956035235834)), (9, np.float64(0.4952510738585344)), (3, np.float64(0.4813371343737055)), (12, np.float64(0.46956931449526157)), (8, np.float64(0.45098948253126997)), (6, np.float64(0.4376000952899765)), (4, np.float64(0.4355582049576015)), (5, np.float64(0.430294133741756)), (1, np.float64(0.4283434707109894)), (10, np.float64(0.41331054139335804)), (7, np.float64(0.41103836745150546)), (2, np.float64(0.3892930136104226))]\n",
      "查询: 什么是'可解释AI'，为什么它被认为很重要？\n",
      "上下文 1:\n",
      "量之间取得平衡是一项关键挑战。\n",
      "为人工智能研发提供资金\n",
      "政府在资助人工智能研发方面发挥着至关重要的作用。公共资金支持基础研究、应用研究和人工智能基\n",
      "础设施的开发。政府投资推动创新，促进合作。\n",
      "国际合作\n",
      "国际合作对于应对人工智能带来的全球挑战和机遇至关重要。这包括分享知识、制定标准和促进负责任\n",
      "的跨国人工智能实践。\n",
      "公众参与和教育\n",
      "让公众参与有关人工智能的讨论，对于建立信任和确保人工智能的发展符合社会价值观至关重要。教育\n",
      "和宣传活动让公众了解人工智能、其影响和潜力。\n",
      "第 1G 章：人工智能与伦理\n",
      "人工智能伦理原则\n",
      "人工智能伦理原则指导着人工智能系统的开发和部署，以确保它们是公平、透明、负责任和有益于社会\n",
      "的。主要原则包括尊重人权、隐私、不歧视和有益性。\n",
      "解决人工智能中的偏见\n",
      "人工智能系统可能会继承和放大其训练数据中存在的偏见，从而导致不公平或歧视性的结果。要解决偏\n",
      "见问题，就必须认真收集数据、设计算法并进行持续监控和评估。\n",
      "透明度和可解释性\n",
      "透明度和可解释性对于建立人工智能系统的信任至关重要。可解释的人工智能（XAI）技术旨在让人工智能\n",
      "决策更容易理解，使用户能够评估其公平性和准确性。\n",
      "隐私和数据保护\n",
      "人工智能系统通常依赖于大量数据，这引发了人们对隐私和数据保护的担忧。确保负责任地处理数据、\n",
      "实施隐私保护技术和遵守数据保护法规至关重要。\n",
      "问责与责任\n",
      "建立人工智能系统的问责制和责任制对于解决潜在危害和确保道德行为至关重要。这包括确定人工智能\n",
      "系统开发者、部署者和使用者的角色和责任。\n",
      "第 20 章：建立对人工智能的信任\n",
      "透明度和可解释性\n",
      "透明度和可解释性是建立对人工智能信任的关键。让人工智能系统易于理解，并深入了解其决策过程，\n",
      "有助于用户评估其可靠性和公平性。\n",
      "稳健性和可靠性\n",
      "确保人工智能系统稳健可靠是建立信任的关键。这包括测试和验证人工智能模型、监控其性能以及解决\n",
      "潜在的漏洞。\n",
      "用户控制和代理\n",
      "赋予用户对人工智能系统的控制权，让他们在与人工智能的互动中拥有自主权，可以增强信任感。这包\n",
      "括允许用户自定义人工智能设置，了解他们的数据是如何被使用的，以及选择退出人工智能驱动的功能\n",
      "。\n",
      "道德设计与开发\n",
      "将道德因素纳入人工智能系统的设计和开发对于建立信任至关重要。这包括进行道德影响评估、让利益相关\n",
      "者参与进来以及遵守道德准则和标准。\n",
      "公众参与和教育\n",
      "让公众参与有关人工智能的讨论，\n",
      "=====================================\n",
      "上下文 2:\n",
      "了解人工智能\n",
      "第 1 章：人工智能简介\n",
      "人工智能（AI）是指数字计算机或计算机控制的机器人执行通常与智能生物相关的任务的能力。该术语\n",
      "经常被用于开发具有人类特有智力过程的系统的项目，如推理、发现意义、概括或从过去的经验中学习\n",
      "的能力。过去几十年来，计算能力和数据可用性的进步大大加快了人工智能的发展和部署。\n",
      "历史背景\n",
      "人工智能的概念已经存在了几个世纪，通常在神话和小说中有所描述。然而，人工智能研究的正式领域\n",
      "始于 20 世纪中期。1956 年的达特茅斯研讨会被广泛认为是人工智能的发源地。早期的人工智能研究侧\n",
      "重于解决问题和符号方法。20 世纪 80 年代，专家系统兴起，而 20 世纪 90 年代和 21 世纪则带来了机器\n",
      "学习和神经网络的进步。最近在深度学习方面取得的突破彻底改变了这一领域。\n",
      "现代观察\n",
      "现代人工智能系统在日常生活中越来越普遍。从 Siri 和 Alexa 等虚拟助手到流媒体服务和社交媒体上的\n",
      "推荐算法，人工智能正在影响着我们的生活、工作和互动方式。自动驾驶汽车、先进的医疗诊断技术和\n",
      "复杂的金融建模工具的发展表明，人工智能的应用范围越来越广。人们对道德影响、偏见和失业的担忧\n",
      "也日益突出。\n",
      "第 2 章：人工智能的核心概念\n",
      "机器学习\n",
      "机器学习（ML）是人工智能的一个子集，其重点是使系统能够在没有明确编程的情况下从数据中学习\n",
      "。ML 算法可以识别模式、进行预测，并随着时间的推移，在接触更多数据后提高性能。\n",
      "监督学习\n",
      "在监督学习中，算法是在有标签的数据上进行训练的，其中输入数据与正确的输出配对。这样，算法就\n",
      "能学习输入和输出之间的关系，并对新的、未见过的数据进行预测。这方面的例子包括图像分类和垃圾\n",
      "邮件检测。\n",
      "无监督学习\n",
      "无监督学习算法是在无标记数据上进行训练的，算法必须在没有明确指导的情况下发现数据中的模式和\n",
      "结构。常见的技术包括聚类（对相似数据点进行分组）和降维（在保留重要信息的同时减少变量数量）\n",
      "。\n",
      "Subscribe to DeepL Pro to translate larger documents.\n",
      "Visit www.DeepL.com/pro for more information.\n",
      "强化学习\n",
      "强化学习是指训练一个代理在环境中做出决策，使奖励最大化。代理通过尝试和错误进行学习，并以奖\n",
      "励或惩罚的形式获得反馈。这种方法可用于游戏、机器人和资源管理\n",
      "=====================================\n"
     ]
    }
   ],
   "source": [
    "# 从JSON文件加载验证数据\n",
    "with open('data/val_zh.json') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# 从验证数据中提取第一个查询\n",
    "query = data[0]['question']\n",
    "\n",
    "# 执行语义搜索，找到查询的前2个最相关文本块\n",
    "top_chunks = semantic_search(query, text_chunks, response.data, k=2)\n",
    "\n",
    "# 打印查询\n",
    "print(\"查询:\", query)\n",
    "\n",
    "# 打印前2个最相关的文本块\n",
    "for i, chunk in enumerate(top_chunks):\n",
    "    print(f\"上下文 {i + 1}:\\n{chunk}\\n=====================================\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基于检索的文本块生成响应"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChatCompletion(id='chatcmpl-485', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='根据提供的上下文信息，“可解释AI”（XAI）是指让人工智能决策更容易理解的技术。这种透明度对于建立公众对人工智能系统的信任至关重要，使用户能够评估其公平性和准确性。\\n\\n因此，可解释AI被认为是重要的原因在于它有助于增强用户对人工智能系统的理解和信任。', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None))], created=1750669043, model='qwen2.5:7b', object='chat.completion', service_tier=None, system_fingerprint='fp_ollama', usage=CompletionUsage(completion_tokens=63, prompt_tokens=1167, total_tokens=1230, completion_tokens_details=None, prompt_tokens_details=None))\n",
      "根据提供的上下文信息，“可解释AI”（XAI）是指让人工智能决策更容易理解的技术。这种透明度对于建立公众对人工智能系统的信任至关重要，使用户能够评估其公平性和准确性。\n",
      "\n",
      "因此，可解释AI被认为是重要的原因在于它有助于增强用户对人工智能系统的理解和信任。\n"
     ]
    }
   ],
   "source": [
    "# 定义AI助手的系统提示\n",
    "system_prompt = \"你是一个严格基于给定上下文回答问题的AI助手。如果答案无法直接从提供的上下文中推导出来，请回答：'我没有足够的信息来回答这个问题。'\"\n",
    "\n",
    "def generate_response(system_prompt, user_message, model=\"qwen2.5:7b\"):\n",
    "    \"\"\"\n",
    "    基于系统提示和用户消息从Ollama模型生成响应。\n",
    "\n",
    "    参数:\n",
    "    system_prompt (str): 指导AI行为的系统提示。\n",
    "    user_message (str): 用户的消息或查询。\n",
    "    model (str): 用于生成响应的模型。默认是\"qwen2.5:7b\"。\n",
    "\n",
    "    返回:\n",
    "    dict: 来自Ollama模型的响应。\n",
    "    \"\"\"\n",
    "    response = client.chat.completions.create(\n",
    "        model=model,\n",
    "        temperature=0,\n",
    "        messages=[\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_message}\n",
    "        ]\n",
    "    )\n",
    "    return response\n",
    "\n",
    "# 基于顶部文本块创建用户提示\n",
    "user_prompt = \"\\n\".join([f\"上下文 {i + 1}:\\n{chunk}\\n=====================================\\n\" for i, chunk in enumerate(top_chunks)])\n",
    "user_prompt = f\"{user_prompt}\\n问题: {query}\"\n",
    "\n",
    "# 生成AI响应\n",
    "ai_response = generate_response(system_prompt, user_prompt)\n",
    "print(ai_response)\n",
    "print(ai_response.choices[0].message.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估AI响应\n",
    "我们将AI响应与预期答案进行比较并分配分数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "评分：1分\n",
      "\n",
      "AI助手的回答准确地解释了“可解释AI”的定义，并指出了其重要性，即增强用户对人工智能系统的理解和信任。这与真实响应中的内容非常接近，因此可以给满分。\n"
     ]
    }
   ],
   "source": [
    "# 定义评估系统的系统提示\n",
    "evaluate_system_prompt = \"你是一个智能评估系统，负责评估AI助手的响应。如果AI助手的响应与真实响应非常接近，分配1分。如果响应相对于真实响应不正确或不令人满意，分配0分。如果响应与真实响应部分一致，分配0.5分。\"\n",
    "\n",
    "# 通过结合用户查询、AI响应、真实响应和评估系统提示来创建评估提示\n",
    "evaluation_prompt = f\"用户查询: {query}\\nAI响应:\\n{ai_response.choices[0].message.content}\\n真实响应: {data[0]['ideal_answer']}\\n{evaluate_system_prompt}\"\n",
    "\n",
    "# 使用评估系统提示和评估提示生成评估响应\n",
    "evaluation_response = generate_response(evaluate_system_prompt, evaluation_prompt)\n",
    "\n",
    "# 打印评估响应\n",
    "print(evaluation_response.choices[0].message.content)"
   ]
  }
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